Google Play Store Scraper — App Data, Dev Email & Reviews | $2/1K | No Proxy

Mobile growth managers, indie developers, and ASO agencies use this actor to track competitor install counts, rating distributions, and developer contacts at scale.

$2.00 per 1,000 app records + $1.00 per 1,000 embedded reviews. Developer email included free when available.

No proxy required. No API key. No login. Uses Google's own embedded AF_initDataCallback data — not scraped HTML classes.

Scrape Google Play Store app details and user reviews without a proxy. Returns rich metadata plus up to 20 user reviews per app, all from Google's own embedded data.

Not affiliated with Google LLC or the Google Play Store.

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What you get

Each app produces one app_meta record plus optional review records:

Field	Example	Notes
app_id	com.spotify.music	Android package name
title	Spotify: Music and Podcasts
developer	Spotify AB
developer_id	6523013212965523194
developer_email	support@spotify.com
developer_website	https://www.spotify.com
category	Music & Audio
category_id	MUSIC_AND_AUDIO
rating_avg	4.33	0–5.0
rating_count	35,670,770	Total ratings
rating_1..5	3,694,193	Per-star count
installs	1,000,000,000+	Formatted string
installs_min	1000000000	Numeric lower bound
price_usd	0.0 / 6.99	0 = free
has_iap	true	In-app purchases flag
content_rating	Teen
description	Full text	Plain text (HTML stripped)
whats_new	Bug fixes...	Latest changelog
updated	2026-05-26T14:23:49+00:00	ISO 8601 UTC
released	2014-05-27T13:12:17+00:00	ISO 8601 UTC
icon_url	https://play-lh.google...	512×512
parse_confidence	1.0	0–1 data quality score

Review fields (when includeReviews: true): review_id, review_author, review_rating, review_text, review_date, review_helpful, review_version.

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Sample output

{
  "record_type": "app_meta",
  "app_id": "com.spotify.music",
  "title": "Spotify: Music and Podcasts",
  "developer": "Spotify AB",
  "developer_email": "support@spotify.com",
  "rating_avg": 4.33,
  "installs": "1,000,000,000+",
  "installs_min": 1000000000,
  "price_usd": 0.0,
  "parse_confidence": 1.0
}

---

Input

{
  "appIds": ["com.spotify.music", "com.whatsapp"],
  "searchTerms": ["meditation app"],
  "country": "US",
  "lang": "en",
  "includeReviews": true
}

• appIds — Android package names. Find in Play Store URL: play.google.com/store/apps/details?id=com.spotify.music
• searchTerms — keyword queries; actor extracts matching app IDs, then scrapes each
• country — 2-letter ISO code (US, GB, DE, FR, JP, CA, AU)
• lang — BCP-47 language (en, de, fr, ja, pt)
• includeReviews — embed up to 20 user reviews per app

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About reviews

Google Play embeds up to 20 reviews per app in the detail page HTML. This actor extracts those reviews for free — no proxy required.

For buyers needing more than 20 reviews per app: Google's pagination endpoint (batchexecute) requires an authenticated browser session or residential proxy. A future version with proxy support may be added; for now this actor delivers the 20 reviews embedded in every app page.

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Why parse_confidence?

Every record includes a parse_confidence score (0.0–1.0). If Google updates its page structure, confidence drops before results go silently empty — you see the warning before it breaks.

Score < 0.5 → check warnings field for details.

vs. competitors

Feature	This actor	compass/google-play-scraper	apidojo/google-play-scraper
Rating distribution (1–5★ counts)	✅	❌	partial
Developer email in output	✅	❌	❌
parse_confidence drift detection	✅	❌	❌
Proxy required	❌ none	❌ none	✅ required

Also available: Apple App Store Scraper — same schema, Apple ecosystem. Run both to compare iOS vs Android market positions.

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Pricing

Pay per result: $2.00 per 1,000 items (app_meta + review records combined). No proxy costs — zero COGS.

Developer contact event (adaptive): an additional small charge applies per app where developer_email is present and non-null. You only pay when the data is actually there — no charge for apps without email.

Worked example:

• 50 apps + 20 reviews each = 50 + 1,000 = 1,050 items → $2.10
• 200 apps (metadata only) = 200 items → $0.40
• 1,000 apps with developer email extracted → base $2.00 + adaptive developer-contact charge per app with email

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FAQ

Do I need a proxy or Google account? No. This actor uses Google's own embedded data (AF_initDataCallback) without any proxy or authentication.

What formats can I export? JSON, CSV, Excel, or JSONL — from the Apify dataset UI or via the REST API.

Can I schedule it to run automatically? Yes. Use Apify Scheduler for daily or weekly runs. Combine with a webhook to Slack or Airtable to track rating and install changes over time.

What if the actor returns empty or partial data? Check parse_confidence in the output. A score below 0.5 means Google may have updated their page structure. The warnings field lists exactly which fields could not be parsed.

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Technical notes

• No proxy required: fetches Google Play HTML with proper browser headers
• No auth required: all data comes from publicly embedded AF_initDataCallback JSON blobs
• Structural parsing: anchors to data positions in the DS blob, not CSS class names
• Zero COGS: buyers pay only the PPE price — no residential proxy costs

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Use with AI agents (MCP)

All actors in this suite are available via the Apify MCP server. Connect to Claude, GPT-4o, or n8n to let AI agents pull live App Store data on demand — market research, competitor monitoring, and ASO tracking automated end-to-end.

MCP config: https://mcp.apify.com/?tools=bovi/google-play-scraper

Also in this suite

• Apple App Store Scraper — same schema, Apple ecosystem. Compare iOS vs Android market positions.
• App Store Reviews Scraper — reviews-only, bulk NLP use case
• App Store Charts Scraper — market surveillance, top charts
• ASO Keyword Rank Tracker — ranking intelligence, 10× cheaper than Sensor Tower
• iOS App Update Tracker — version monitoring, change detection
• F-Droid Scraper — FOSS/privacy research

Integrations

Built for mobile-growth managers and ASO agencies benchmarking competitor apps, ratings, and developer contacts — the JSON/dataset output drops into the tools you already run, no glue code:

• n8n / Make / Zapier — trigger a run or pipe every new dataset item into 500+ apps (Google Sheets, Airtable, Slack, HubSpot, your database) with no code: n8n, Make, Zapier.
• Webhooks — fire your own endpoint the moment a run finishes, to push results straight into your pipeline (docs).
• MCP server — expose this actor as a tool to Claude, Cursor, or any MCP client so an AI agent can pull this data mid-conversation (guide).
• API & SDKs — fetch the dataset as JSON, CSV, or Excel through the Apify REST API or the Python / JS SDKs.

See all Apify integrations.

Legal

Scraping publicly accessible web pages is generally lawful under established case law (hiQ v. LinkedIn). This actor accesses only public, unauthenticated pages. Not affiliated with Google LLC.

More scrapers from our toolkit

Building a data pipeline? These actors pair well with this one — each runs on your own Apify account with the same pay-per-result pricing, no subscription:

• Google Search (SERP) Scraper
• Similarweb Traffic Scraper
• App Store Scraper
• App Update Tracker
• Appstore Charts
• Appstore Reviews

Chain any of them together from the Integrations tab (the Run succeeded trigger) to build a multi-step workflow — one actor's output feeds the next.

Usage statistics

This Actor creates a small, content-free summary at the end of each run. It is used only to monitor reliability and improve this Actor. A copy is saved as USAGE_STATS in your own Apify key-value store, so you can see the exact record created for your run.

Set disableUsageStats to true in the input to opt out. Nothing is sent then; your USAGE_STATS record only says that statistics were disabled.

Only these fields are recorded:

• schema version, Actor name and build number;
• UTC start and finish hour (not a precise timestamp);
• run duration, number of results and time to the first result, each as a coarse range;
• whether the result was empty, the end status, and an error type from a fixed list;
• memory setting and counts of charged events;
• names of the input fields you set, never their values;
• the selected option for input fields that offer a fixed list of choices (for example a sort order).

We do not collect input text, search terms, URLs, domains, usernames, email addresses, names, proxy credentials, tokens, scraped records, output items, raw error messages, stack traces, or hashes of any of those values. Records are kept for no longer than 13 months, used only as aggregated operational statistics, and never sold or shared.

Additional fields (Phase 2)

This Actor also records your Apify user ID, whether Apify marks the account as paying, the size range of list inputs, the selected country when the input offers a fixed list of countries, and one category from a fixed Actor taxonomy. We use these fields only for aggregate reliability, repeat-use and cross-Actor analysis; reports suppress any cell with fewer than five distinct users.

The same disableUsageStats: true input flag turns these fields off too. The user ID is removed after 13 months; we do not export, sell, share, or attempt to re-identify this data.

Run-outcome signals (v2)

To learn whether a run did what it was asked to do, the record also holds a few more coarse ranges and yes/no flags. None of them contains content:

• the result limit you asked for (a range, when the input has one) and what share of it was delivered;
• results delivered per input item you listed (a range);
• output quality as ranges: how fully the result fields were filled, the share of rows that look like errors, the share of duplicate rows, and how many different fields appeared. These are counted in memory while results are saved; no result content is kept;
• how the run was started (console, API, schedule, webhook, another Actor);
• how it ended: stopped by you, timed out, reached the requested limit, stopped by the charge limit, and how many times the platform moved the run;
• if this Actor reports it: how many items to process worked or failed (ranges) and one failure reason from a fixed list;
• a short code made from the names of the input fields you set, never their values.

Repeat-run fingerprint (v2)

When your Apify user ID is recorded (see above), the record also holds an 8-character one-way code made from your input (proxy settings left out) and this Actor's name. It only lets us see that the same account ran the same input again soon after an unsatisfying run; we never see the input itself. It is stored only in the database, never published, and reports use it in aggregate with the same five-user minimum. It is the one exception to the statement above that no hashes are collected, and disableUsageStats: true turns it off.